# Plotly — Interactive Graphing Library for Python

> Plotly.py creates publication-quality interactive charts that render in browsers, notebooks, and Dash apps. It supports over 40 chart types from scatter plots to 3D surfaces.

## Install

Save as a script file and run:

# Plotly — Interactive Graphing Library for Python

## Quick Use
```bash
pip install plotly
python -c "
import plotly.express as px
fig = px.scatter(x=[1,2,3,4], y=[10,11,12,13], title='Quick Demo')
fig.show()
"
```

## Introduction
Plotly.py is a high-level graphing library that produces interactive, browser-based visualizations. Built on top of Plotly.js (which uses D3.js and WebGL), it renders charts that users can zoom, pan, hover, and export — all from a Python API that works in scripts, Jupyter notebooks, and Dash applications.

## What Plotly Does
- Creates over 40 interactive chart types (scatter, bar, line, heatmap, 3D, maps, etc.)
- Renders in Jupyter notebooks, standalone HTML files, or Dash web apps
- Provides Plotly Express for one-line chart creation from DataFrames
- Supports WebGL rendering for charts with millions of data points
- Exports to static images (PNG, SVG, PDF) via Kaleido or Orca

## Architecture Overview
Plotly.py generates JSON figure specifications conforming to the Plotly.js schema. The Python library constructs a `Figure` object containing `data` traces and a `layout` dictionary. When displayed, it serializes this to JSON and hands it to Plotly.js in the browser for rendering. Plotly Express is a high-level wrapper that maps DataFrame columns to figure properties using sensible defaults.

## Self-Hosting & Configuration
- Install with `pip install plotly` (no additional dependencies for notebook rendering)
- Add `pip install kaleido` for static image export support
- Use `plotly.io.templates` to set a default theme (plotly, plotly_dark, seaborn, etc.)
- Configure default renderers with `plotly.io.renderers.default` for your environment
- Integrate with Dash for full web application deployment

## Key Features
- Plotly Express provides concise one-liner APIs for common chart types
- Built-in faceting, animation, and trendline support in Express
- WebGL-accelerated scatter plots (Scattergl) handle millions of points smoothly
- Mapbox and Maplibre integration for geographic data visualization
- Subplots, dual axes, and shared legends for complex multi-panel figures

## Comparison with Similar Tools
- **Matplotlib** — static plots by default; Plotly is interactive out of the box but has a larger footprint
- **Seaborn** — statistical visualization on Matplotlib; Plotly Express offers similar convenience with interactivity
- **Bokeh** — interactive plotting with its own server; Plotly integrates tightly with Dash for applications
- **Altair** — declarative grammar based on Vega-Lite; Plotly supports more chart types and WebGL rendering
- **Apache ECharts** — JavaScript-first charting; Plotly.py provides a native Python API without writing JS

## FAQ
**Q: Does Plotly work offline?**
A: Yes. Charts render entirely in the browser using bundled JavaScript. No internet connection is needed after installation.

**Q: Can Plotly handle large datasets?**
A: Use `Scattergl`, `Heatmapgl`, or other WebGL trace types for datasets with hundreds of thousands to millions of points.

**Q: How do I save a chart as a PNG?**
A: Install kaleido (`pip install kaleido`) and call `fig.write_image("chart.png")`.

**Q: Is Plotly free?**
A: Yes. Plotly.py and Plotly.js are MIT-licensed and fully open source.

## Sources
- https://github.com/plotly/plotly.py
- https://plotly.com/python/

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Source: https://tokrepo.com/en/workflows/9107f42c-40a1-11f1-9bc6-00163e2b0d79
Author: Script Depot